Inconsistency measure of database with similarity relation

As one of the principles to select and appraise data mining algorithm, the inconsistency measure of database received much attention in classification rules discovering. But the classical measure based on information entropy will not meet those needs with the further study of incomplete database since the requirement of equivalence relation may not be satisfied in such condition. This paper gives a method to found information granularity with belief and plausibility measure based on the similarity relation and evidence theory. At the same time, inconsistency measures which are similar to inconsistent and confusion degree of fuzzy entropy are proposed with the proving of their some character. From the proving and simulation, it shows the proposed method will give a well description of inconsistency in incomplete database, and when there is no data missing, it will gives a same result as the previous studies.